Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system
Abstract Wind turbine faults, including electrical, mechanical or aerodynamics-related, can potentially reduce operational efficiency, causing downtimes or, in some cases, leading to severe damage. Hence, timely detection of these operational anomalies is crucial for optimizing performance and reduc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97663-3 |
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| _version_ | 1850146267748892672 |
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| author | Muhammad Irfan Nabeel Ahmed Khan Muhammad Abubakar Zohaib Mushtaq Tomasz Jakubowski Paweł Sokołowski Grzegorz Nawalany Saifur Rahman |
| author_facet | Muhammad Irfan Nabeel Ahmed Khan Muhammad Abubakar Zohaib Mushtaq Tomasz Jakubowski Paweł Sokołowski Grzegorz Nawalany Saifur Rahman |
| author_sort | Muhammad Irfan |
| collection | DOAJ |
| description | Abstract Wind turbine faults, including electrical, mechanical or aerodynamics-related, can potentially reduce operational efficiency, causing downtimes or, in some cases, leading to severe damage. Hence, timely detection of these operational anomalies is crucial for optimizing performance and reducing maintenance costs. The following study explores the application of Supervisory Control and Data Acquisition (SCADA) data for fault detection and diagnosing different operational states in wind turbines by focusing on environmental and operational factors which affect the performance. An efficient noise-resilient classification framework is proposed which includes a novel Perturbed-Random Forest (P-RF) algorithm to diagnose operational states with high accuracy even under noisy conditions. Seasonal discrepancies in power generation are analyzed using theoretical and actual power analysis. Further, distributions of features are realized via kernel density estimation and efficiency metrics in order to identify performance inefficiencies. The P-RF algorithm achieved 99.72% accuracy in diagnosing the status of the SCADA-based wind turbine although with a slightly higher computational complexity than a baseline Random Forest algorithm. Multiple Evaluation metrics were employed to assess the performance of the proposed model under different signal-to-noise conditions. |
| format | Article |
| id | doaj-art-2b190b2784794a54bbdcf1a8faa89d3b |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2b190b2784794a54bbdcf1a8faa89d3b2025-08-20T02:27:53ZengNature PortfolioScientific Reports2045-23222025-04-0115111910.1038/s41598-025-97663-3Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition systemMuhammad Irfan0Nabeel Ahmed Khan1Muhammad Abubakar2Zohaib Mushtaq3Tomasz Jakubowski4Paweł Sokołowski5Grzegorz Nawalany6Saifur Rahman7Electrical Engineering Department, College of Engineering, Najran UniversityCenter for AI & Big Data, Namal UniversityDepartment of Computer Science, Lahore Garrison UniversityDepartment of Electrical Electronics and Computer Systems, College of Engineering and Technology, University of SargodhaDepartment of Machine Operation, Ergonomics and Production Processes, Faculty of Production and Power Engineering, University of Agriculture in KrakowDepartment of Rural Building, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in KrakowDepartment of Rural Building, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in KrakowElectrical Engineering Department, College of Engineering, Najran UniversityAbstract Wind turbine faults, including electrical, mechanical or aerodynamics-related, can potentially reduce operational efficiency, causing downtimes or, in some cases, leading to severe damage. Hence, timely detection of these operational anomalies is crucial for optimizing performance and reducing maintenance costs. The following study explores the application of Supervisory Control and Data Acquisition (SCADA) data for fault detection and diagnosing different operational states in wind turbines by focusing on environmental and operational factors which affect the performance. An efficient noise-resilient classification framework is proposed which includes a novel Perturbed-Random Forest (P-RF) algorithm to diagnose operational states with high accuracy even under noisy conditions. Seasonal discrepancies in power generation are analyzed using theoretical and actual power analysis. Further, distributions of features are realized via kernel density estimation and efficiency metrics in order to identify performance inefficiencies. The P-RF algorithm achieved 99.72% accuracy in diagnosing the status of the SCADA-based wind turbine although with a slightly higher computational complexity than a baseline Random Forest algorithm. Multiple Evaluation metrics were employed to assess the performance of the proposed model under different signal-to-noise conditions.https://doi.org/10.1038/s41598-025-97663-3Perturbed-Random forestSupervisory controlData acquisitionKernel densityWind turbineFault detection |
| spellingShingle | Muhammad Irfan Nabeel Ahmed Khan Muhammad Abubakar Zohaib Mushtaq Tomasz Jakubowski Paweł Sokołowski Grzegorz Nawalany Saifur Rahman Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system Scientific Reports Perturbed-Random forest Supervisory control Data acquisition Kernel density Wind turbine Fault detection |
| title | Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system |
| title_full | Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system |
| title_fullStr | Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system |
| title_full_unstemmed | Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system |
| title_short | Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system |
| title_sort | design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system |
| topic | Perturbed-Random forest Supervisory control Data acquisition Kernel density Wind turbine Fault detection |
| url | https://doi.org/10.1038/s41598-025-97663-3 |
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